most citedBayesian Optimization Meets Riemannian Manifolds in Robot Learning

11 citations · 11 across the 3 of their papers we have counts for

collaborators

5 papers

cs.RO2020

Learning and Sequencing of Object-Centric Manipulation Skills for Industrial Tasks

Leonel Rozo, Meng Guo, Andras G. Kupcsik +8

Enabling robots to quickly learn manipulation skills is an important, yet challenging problem. Such manipulation skills should be flexible, e.g., be able adapt to the current works…

cs.RO2020

Analysis and Transfer of Human Movement Manipulability in Industry-like Activities

Noémie Jaquier, Leonel Rozo, Sylvain Calinon

Humans exhibit outstanding learning, planning and adaptation capabilities while performing different types of industrial tasks. Given some knowledge about the task requirements, hu…

cs.RO201911 cited

Bayesian Optimization Meets Riemannian Manifolds in Robot Learning

Noémie Jaquier, Leonel Rozo, Sylvain Calinon +1

Bayesian optimization (BO) recently became popular in robotics to optimize control parameters and parametric policies in direct reinforcement learning due to its data efficiency an…

cs.RO2019

Interactive Trajectory Adaptation through Force-guided Bayesian Optimization

Leonel Rozo

Flexible manufacturing processes demand robots to easily adapt to changes in the environment and interact with humans. In such dynamic scenarios, robotic tasks may be programmed th…

cs.RO2019

Hierarchical Reinforcement Learning for Concurrent Discovery of Compound and Composable Policies

Domingo Esteban, Leonel Rozo, Darwin G. Caldwell

A common strategy to deal with the expensive reinforcement learning (RL) of complex tasks is to decompose them into a collection of subtasks that are usually simpler to learn as we…